{
"DOI": "10.5281/zenodo.7339113",
"abstract": "ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions.\u00a0This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.\n\n\nHere we provide the functions generated, the unique equations, and the mappings between all equations and unique ones\u00a0using different sets of basis functions. These are:\n\n\n\n\t\n\"core_maths\":\u00a0\\(\\{x, a, {\\rm inv}, +, -, \\times, \\div, {\\rm pow} \\}\\)\n\t\n\"ext_maths\":\u00a0\\(\\{x, a, {\\rm inv}, \\sqrt{\\cdot}, {\\rm square}, \\exp, +, -, \\times, \\div, {\\rm pow} \\}\\)\n\n\n\nwhere \\(x\\)\u00a0is the input variable and \\(a\\)\u00a0denotes a constant.\n\n\nOne can fit these functions to a data set of interest by using the ESR package.",
"author": [
{
"family": "Bartlett",
"given": "Deaglan J."
},
{
"family": "Desmond",
"given": "Harry"
},
{
"family": "Ferreira",
"given": "Pedro G."
}
],
"id": "7339113",
"issued": {
"date-parts": [
[
"2022",
"11",
"20"
]
]
},
"publisher": "Zenodo",
"title": "Exhaustive Symbolic Regression Function Sets",
"type": "dataset"
}